It’s no secret that AI, machine learning and big data are going to transform how financial institutions protect their customers from the influx of card fraud that continues to rise each year. It’s why the Rippleshot team decided to bring a new solution to the market six years ago.
Financial institutions are getting faster and better at preventing, detecting and stopping fraud, thanks to help from more sophisticated software. But fraudsters are getting faster. As everyone knows, card fraud is skyrocketing and reissuance costs are at an all time high as the price to produce EMV chip cards rises. Banks and credit unions are absorbing these costs, while still having to learn how to keep up at the rate fraudsters are evolving their techniques.
Our sales pitch will tell you all you need to know about why we believe in our mission of collaboratively fighting fraud with data. Through the power of machine learning and big data, we help banks and credit unions reduce fraud, decrease re-issuance costs, and minimize customer/member impact. The quick answer of how we do this is that we’re able to analyze millions of card transactions daily, proactively helping FIs detect where gaps exist and how then can address the biggest risks first in order to achieve those three core goals of any organizations highlighted above.
Our automated, big data approach helps financial institutions stop the spread of fraud in near real-time. By pinpointing fraud at its source, this helps FIs get ahead of the problems that keep executive teams up at night. Our solution detects compromised merchants and identifies at-risk cards. It also indicates the chance that specific card will actually go fraudulent.
This is all made possible because we have the relationships to get the information banks and credit unions to get ahead of breaches faster. We leverage machine learning and combined data from our network of banks, credit unions, and processors. That allows us to monitor roughly 8.7 trillion transactions annually.
This data analytics approach equips issuers with the tools to understand what’s happening across their own card portfolio — and how to detect risk. Quickly. We’ll tell you (personally) where fraud is starting to emerge, how bad it’s going to be and the number of cards likely to be breached. Our team is hyper-focused on the success of our customers and the relations with their customers. We’re a group of nerdy, tech-talking data scientists, but we also know how to work with our customers to understand their biggest pain points.
At the end of the day it’s all about having the data and knowing how to use it to proactively detect card fraud. Most banks and credit unions don’t have enough data to fight fraud effectively and it is not easily accessible. Financial institutions are strapped for IT resources making technology implementation challenging.
How Rippleshot’s Fraud Analytics Approach Works (The Skinny Version)
Fraud and fraud patterns are evolving and change more rapidly than financial institutions can keep pace with. That’s where better data analytics tools bridge the gap by helping banks and credit unions detect breaches faster, and at their source. AKA: Where Rippleshot fits into the picture. Beyond just our technical specs, we’ve also got a product support team equipped with data scientists, a customer success manager, and a product guru ready to make sure our clients are able to maximize the benefits of our fraud analytics solution.
Sophisticated tech and big data is driving the change you’re seeing across the financial services market, but the real differentiator in any solution is the people powering that platform. Get to know our team and you’ll understand why that really matters.
That’s just the tip of the iceberg. Our VP of Sales, John O’Neill, would love to know more about your pain points and how we can help. Ready to learn more? Reach out to our team and get your own demo inside our platform today.
You have fraud frustrations? We have the solutions. Let's discuss what you are dealing with and we can learn more and share how we can help.